[email protected]
Author
Contributions by role
Author 2
Tingli Su
School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Summary
Tingli Su received her B.E. degree in Mechatronic Engineering and the Ph.D. degree in the direction of Control Science and Engineering from Beijing Institute of Technology, Beijing, China, in 2007 and 2013. During the period of 2009.10-2012.9, she had a total of 2 years and a half working as an academic collaborator in University of Bristol, U.K. and finished most of her Ph.D. research there. Since 2013 she has been with School of Computer and Information Engineering, Beijing Technology and Business University as a Lecturer, and was promoted to be the Associate Professor in October, 2018. Her research interests include multi-sensor fusion, statistical signal processing, robust filtering, Bayesian theory, target tracking and dynamic analysis. In particular, her present major interest is multi-sensor fusion, Bayesian estimation and big data tendency analysis.
Edited Journals
IECE Contributions
Articles
2
Edited
0
Open Access | Research Article | 22 March 2025
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
by
Xuebo Jin
,
Heran Fu
,
Jianlei Kong
,
Huijun Ma
,
Yuting Bai
,
Tingli Su
Chinese Journal of Information Fusion | Volume 2, Issue 1: 38-58, 2025 | DOI:10.62762/CJIF.2025.919344
Abstract
With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tai... More >
Graphical Abstract
A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications
Free Access | Research Article | 08 June 2024 | Cited: 3
GPS Tracking Based on Stacked-Serial LSTM Network
by
Xuebo Jin
,
Songzheng Liu
,
Jianlei Kong
,
Yuting Bai
,
Tingli Su
,
Huijun Ma
Chinese Journal of Information Fusion | Volume 1, Issue 1: 50-62, 2024 | DOI:10.62762/CJIF.2024.361889
Abstract
Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed... More >
Graphical Abstract
GPS Tracking Based on Stacked-Serial LSTM Network